Metadata-Version: 2.3
Name: swarmauri_vectorstore_redis
Version: 0.7.1
Summary: Swarmauri Redis Vector Store
License: Apache-2.0
Author: Jacob Stewart
Author-email: jacob@swarmauri.com
Requires-Python: >=3.10,<3.13
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: redis (>=4.0)
Requires-Dist: redisearch
Requires-Dist: swarmauri_base
Requires-Dist: swarmauri_core
Requires-Dist: swarmauri_embedding_doc2vec
Requires-Dist: swarmauri_standard
Description-Content-Type: text/markdown


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---

# Swarmauri Vectorstore Redis

A Redis-based vector store implementation for the Swarmauri SDK that enables efficient storage and retrieval of document embeddings.

## Installation

```bash
pip install swarmauri_vectorstore_redis
```

## Usage

Basic example of using RedisVectorStore:

```python
from swarmauri.vector_stores.RedisVectorStore import RedisVectorStore
from swarmauri.documents.Document import Document

# Initialize the vector store
vector_store = RedisVectorStore(
    redis_host="localhost",
    redis_port=6379,
    redis_password="your_password",
    embedding_dimension=8000
)

# Add documents
document = Document(
    id="doc1",
    content="Sample document content",
    metadata={"category": "sample"}
)
vector_store.add_document(document)

# Retrieve similar documents
similar_docs = vector_store.retrieve("sample content", top_k=5)
```

## Want to help?

If you want to contribute to swarmauri-sdk, read up on our [guidelines for contributing](https://github.com/swarmauri/swarmauri-sdk/blob/master/contributing.md) that will help you get started.


